Image-based character recognition

ABSTRACT

Various embodiments enable a device to perform tasks such as processing an image to recognize and locate text in the image, and providing the recognized text an application executing on the device for performing a function (e.g., calling a number, opening an internet browser, etc.) associated with the recognized text. In at least one embodiment, processing the image includes substantially simultaneously or concurrently processing the image with at least two recognition engines, such as at least two optical character recognition (OCR) engines, running in a multithreaded mode. In at least one embodiment, the recognition engines can be tuned so that their respective processing speeds are roughly the same. Utilizing multiple recognition engines enables processing latency to be close to that of using only one recognition engine.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.13/688,772, filed on Nov. 29, 2012, now U.S. Pat. No. 9,043,349 B1,issued May 26, 2015, of which the full disclosure of this application isincorporated herein by reference for all purposes.

BACKGROUND

Optical character recognition (OCR) systems are generally used to detecttext present in an image and to convert the detected text into itsequivalent electronic representation. In order to accurately recognizetext with a conventional OCR engine, the image typically needs to be ofa high quality. The quality of the image depends on various factors suchas the power of the lens, light intensity variation, relative motionbetween the camera and text, focus, and so forth. Generally, an OCRengine can detect a majority of text characters in good quality images,such as images having uniform intensity, no relative motion, and goodfocus. However, even with good quality images, conventional OCR enginesare still often unable to accurately detect all text characters. Thisimprecision is further exacerbated when attempting to recognize textfrom lesser quality images, such as images containing variations inlighting, shadows, contrast, glare, blur, and the like. As technologyadvances and as people are increasingly using portable computing devicesin a wider variety of ways, it can be advantageous to adapt the ways inwhich images are processed by an OCR engine in order to improve textrecognition precision.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments in accordance with the present disclosure will bedescribed with reference to the drawings, in which:

FIG. 1 illustrates an example visual representation of a process ofrecognizing text in accordance with at least one embodiment;

FIG. 2 illustrates an example situation where a user is attempting torecognize text with a computing device in accordance with variousembodiments;

FIG. 3 illustrates another example visual representation of a process ofrecognizing text in accordance with at least one embodiment;

FIG. 4 illustrates an example environment in which various embodimentscan be implemented;

FIG. 5 illustrates an example process of recognizing text in accordancewith at least one embodiment;

FIGS. 6A and 6B illustrate an example computing device that can be usedto implement aspects of various embodiments;

FIG. 7 illustrates example components that can be used with a devicesuch as that illustrated in FIGS. 6A and 6B; and

FIG. 8 illustrates another example environment in which variousembodiments can be implemented.

DETAILED DESCRIPTION

Systems and methods in accordance with various embodiments of thepresent disclosure may overcome one or more of the aforementioned andother deficiencies experienced in conventional approaches to recognizingtext in an image. In particular, various approaches enable a device toperform tasks such as processing an image to recognize and locate textin the image, and providing the recognized text an application executingon the device for performing a function (e.g., calling a number, openingan internet browser, etc.) associated with the recognized text. In atleast one embodiment, processing the image includes substantiallysimultaneously or concurrently processing the image with at least tworecognition engines, such as at least two optical character recognition(OCR) engines, running in a multithreaded mode. In at least oneembodiment, the recognition engines can be tuned so that theirrespective processing speeds are roughly the same. Utilizing multiplerecognition engines enables processing latency to be close to that ofusing only one recognition engine.

In at least one embodiment, recognized text from each recognition enginegoes through a confidencing module, which determines the probabilityassociated with the accuracy of the recognized text. In order todetermine the probability or confidence score, the confidencing modulemay take into account various attributes. For example, determining thata string of text corresponds to a word in a dictionary will increase therecognized text's confidence score. If the word contains incoherentpatterns, such as a high frequency of repeating the same character andthe like, the presence of those patterns will decrease the recognizedtext's confidence score.

After the confidence scores for the recognized text have beendetermined, a component such as a combination module determines aconsensus string of text that is a compilation of the recognized textfrom each recognition engine weighted by their respective confidencescores. In order to accomplish this, a correspondence between therecognized text from each recognition engine is established. In thisexample, each engine will report coordinates of a bounding box for therecognized text. The bounding box for the recognized text is then usedto align the recognized text from each recognition engine to determinethe correspondence. An overlap percentage of the bounding boxes can beused to map recognized text from one recognition engine to recognizedtext from another recognition engine, for example. Each combined word orsting within the recognized text then is assigned a final confidencescore based on a combination function, such as a linear function, thatis a combination of each recognition engine weighted by their respectiveconfidence scores and/or past recognition accuracy. Further, text can berecognized by comparing multiple images or image frames that include thesame text.

In at least one embodiment, image capture can be performed using asingle image, multiple images, periodic imaging, continuous imagecapturing, image streaming, and the like. For example, the computingdevice can capture multiple images (or video) of text in a continuousmode and provide at least a portion of the same to the recognitionengines to separately recognize text from multiple images. The multipleOCR outputs corresponding to recognized text from the multiple imagescan then be compared to either verify image details or to capturedetails that have been obscured or missed in one image or frame. Inanother example, a single image can be provided to the recognitionengines either in real-time or at a later time compared to when theimage was captured, such as a previously captured image stored in aphoto gallery. Accordingly, at least a portion of these tasks can beperformed on a portable computing device or using at least one resourceavailable across a network as well.

Various other functions and advantages are described and suggested belowas may be provided in accordance with the various embodiments.

FIG. 1 illustrates an example visual representation of a process 100 ofrecognizing text in accordance with at least one embodiment. In variousembodiments, an image 102 is obtained and may undergo variouspreprocessing techniques. A processing component processes the image todetect text in the image 102. For example, the processing component canimplement algorithms that detect and recognize the location of text inthe image 102, and the region of the image 102 that includes the textcan be selected or cropped to remove irrelevant portions of the image102 and to highlight relevant regions containing text. The relevantregions can be binarized, and, thereafter, provided or communicated to aserver. Alternatively, in accordance with at least one embodiment, agrey scale image, color image or any other image (selected/cropped orotherwise not selected/cropped) can be communicated to the server (orremain on the portable computing device) for further processing.

In various embodiments, detecting text in the image 102 can includelocating regions of extremes (e.g., regions of sharp transitions betweenpixel values) such as the edges of letters. The regions of extremes, orthe maximally stable extremal regions, can be extracted and analyzed todetect characters, where the detected characters can be connected and/oraggregated. A text line algorithm can be used to determine theorientation of the connected characters, and once the orientation of thecharacters is determined, a binary mask of the region containing thecharacters can be extracted. The binary mask can be converted into ablack white representation, and the black white representation can becommunicated to an optical character recognition engine (OCR) or othertext recognition engine for further processing. In accordance withvarious embodiments, the binary mask is provided to a first recognitionengine 104 a, a second recognition engine 104 b, and an n^(th)recognition engine 104 n for concurrent character recognition processingin a multithreaded mode. In at least one embodiment, each recognitionengine (104 a, 104 b, 104 n) is tuned so that their respectiveprocessing speeds are roughly the same to within an allowable orreasonable deviation. Tuning the processing speeds of the recognitionengines (104 a, 104 b, 104 n) enables processing latency to be close tothat of using only one recognition engine, in at least one example.

After text is recognized by recognition engines (104 a, 104 b, 104 n),the recognized text is assigned a confidence score. In at least oneembodiment, the recognized text from each recognition engine (104 a, 104b, 104 n) goes through a respective confidencing module (106 a, 106 b,106 n), which determine a probability associated with the accuracy ofthe recognized text. The confidencing modules (106 a, 106 b, 106 n) donot know whether any word or character is correct or not and, therefore,a confidence score is assigned thereto. In one example, in order todetermine a respective confidence score, each respective confidencingmodule (106 a, 106 b, 106 n) includes a conversion table based on thestatistical analysis of comparing testing results against the groundtruth of one or more training sets of known text. The conversion tableis then used to determine the confidence score for future unknown textfrom an image. Various other approaches for determining confidence canbe used as well as discussed or suggested elsewhere herein.

In at least one embodiment, the confidencing modules (106 a, 106 b, 106n) can calculate confidence scores for each detected character, whichcan then be extended to each word or page. In at least one example, theconfidencing modules (106 a, 106 b, 106 n) use algorithms eitherassociated with the recognition engine (104 a, 104 b, 104 n) or as anexternal customized process. The confidence scores can vary betweendifferent recognition engines depending on a number of differentvariables including the font style, font size, whether the text isbolded, underlined, or italicized, and the like. Further, theconfidencing modules (106 a, 106 b, 106 n) may take various otherattributes into account. For example, determining whether a string oftext is a word in a dictionary can increase the recognized text'sconfidence score or, if the word contains incoherent patterns, such as ahigh frequency of repeating the same character and the like, willdecrease the recognized text's confidence score.

After the confidence scores for the recognized text have beendetermined, a combination module 108 determines a consensus string oftext that is a compilation of the recognized text from each recognitionengine (104 a, 104 b, 104 n) weighted by their respective confidencescores. In order to accomplish this, a correspondence between therecognized text from each recognition engine (104 a, 104 b, 104 n) isestablished. In this example, each engine (104 a, 104 b, 104 n) willreport coordinates of a bounding box for the recognized text. Thebounding box for the recognized text is then used to align therecognized text from each recognition engine (104 a, 104 b, 104 n) todetermine a correspondence. An overlap percentage of the bounding boxescan be used to map recognized text from one recognition engine torecognized text from another recognition engine. If the recognized textreceived from each recognition engine (104 a, 104 b, 104 n) is notidentical, then each word within the recognized text is assigned a finalconfidence score based a combination function, such as a linearfunction, that is a combination of each recognition engine weighted by arespective confidence score. Other factors, such as past performance ofa particular engine can also be factored into the weighting or linearfunction. Thereafter, the recognized text with the highest finalconfidence score is selected and can be subsequently used by anapplication, for example, as input for a search engine or otherapplication.

FIG. 2 illustrates an example situation 200 in which a user isattempting to recognize text on a window 204 of a business. Although asmart phone is shown, it should be understood that various other typesof electronic or computing devices that are capable of determining andprocessing input can be used in accordance with various embodimentsdiscussed herein. These devices can include, for example, tabletcomputers, notebook computers, desktop computers, personal dataassistants, electronic book readers, video gaming controllers, andportable media players, among others. In accordance with variousembodiments, instead of manually typing the phone number to call thebusiness or manually typing the business name into a search engine ofdevice's web browser, the user walking down a street could point thecamera of the computing device 202 at the text to recognize andsubsequently provide the text to the phone application to initiate acall or to the browser as a shortcut to navigate the user thereto.

In this example, however, since the user is walking, obtaining anaccurate OCR output can be a challenge. The outside world is filled withless than ideal conditions, such as poor or varied lighting, movementduring image capture, and other circumstances, that make capturing animage ideal for processing by a conventional optical characterrecognizer (OCR) difficult. Further, given people's busy schedules,taking the time and care to capture an ideal image in a movingenvironment, such as in a user's hand, is not necessarily practical andlikely not to be expected. Since different algorithms and recognitionengines have different strengths and weaknesses, it can be advantageousto integrate information from multiple recognition engines.

FIG. 3 illustrates an example visual representation of a process 300 ofrecognizing text in accordance with at least one embodiment. In thisexample, an image 302 captured by the user from FIG. 2 is communicatedto three recognition engines (304 a, 304 b, 304 n) for simultaneous orconcurrent processing. In this example, the recognized text (306 a, 306b, 306 n) from each of the three recognition engines (304 a, 304 b, 304n) is slightly different for the same input image 302 based ondifferences in the recognition algorithm, for example. In this example,the recognized text (306 a, 306 b, 306 n) from each recognition engine(304 a, 304 b, 304 n) is run through confidencing modules (308 a, 308 b,308 n) to determine a probability or confidence score associated withthe accuracy of the recognized text. In this example, the threerecognition engines (304 a, 304 b, 304 n) have determined confidencevalues (310 a, 310 b, 310 n) of 0.80, 0.70, and 0.85 for theirrespective recognized text from the image 302. In at least oneembodiment, a consensus string of text is selected in a combination step312 based on a function, such as a linear combination of the recognizedtext (306 a, 306 b, 306 n) weighted by their confidence values (310 a,310 b, 310 n). Other weighting factors can also be applied to thefunction. For example, the first recognition engine 304 a includes moreerrors or misreads than the second recognition engine 304 b that hasonly one error. If the first recognition engine 304 a continuallyprovides confidence scores higher than other recognition engines thathave fewer errors, the weight of the first recognition engine 304 a canbe discounted. Conversely, if a recognition engine continually provideslower confidence scores relative to other, less accurate, recognitionengines, that recognition engine can be weighted higher or the otherscould be discounted, for example.

FIG. 4 is an example environment 400 in which a user can utilize acomputing device to recognize text, in accordance with variousembodiments. It should be understood that the example system is ageneral overview of basic components, and that there can be manyadditional and/or alternative components utilized as known or used inthe art for recognizing text in multiple images. In this example, a useris able to utilize a client device 402, such as a personal computer,tablet computer, smart phone, and the like, to access an OpticalCharacter Recognition system or service 406 over at least oneappropriate network 404, such as a cellular network, the Internet, oranother such network for communicating digital information. The clientdevice 402 can capture one or more images (or video) of text and sendthe images to the Optical Character Recognition system or service 406over the at least one appropriate network 404. The Optical CharacterRecognition system 406 includes an image-processing module 408 that canapply different operators or techniques to pre-process the images beforesubmitting the images to one or more optical character recognitionmodules 410. Examples of the operators include a Laplacian-or-Gaussianfilter, thresholding filters, and so forth, which enhance or mitigatedifferent characteristics of the images. Examples of thesecharacteristics include intensity, blurriness, and so forth. Afterpre-processing, the one or more recognition engines of the opticalcharacter recognition module 410 concurrently recognizes text from theimage to produce multiple recognized text outputs. In at least oneembodiment, a processor can analyze the recognized text using a database412 of words in order to improve the recognition. The database 412includes a set of words which the processor can search for matchescorresponding to words present in the recognized text. At least aportion of these tasks can be performed on a portable computing deviceor by using at least one resource available across a network as well. Inat least some embodiments, an OCR application will be installed on theclient device 402, such that much of the processing, analyzing, or othersuch aspects can be executed on the client device. Various processingsteps can be performed by the client device 402, by the OpticalCharacter Recognition system 406, or a combination thereof. Therefore,it should be understood that the components and capabilities of theOptical Character Recognition system 406 could wholly or partly resideon the client device 402.

FIG. 5 illustrates an example process 500 for recognizing text in animage with a computing device that can be used in accordance withvarious embodiments. It should be understood that, for this and otherprocesses discussed herein, there can be additional, fewer, oralternative steps, performed in similar or alternative steps, or inparallel, within the scope of the various embodiments unless otherwisestated. In this example, an image captured by a camera of a computingdevice is obtained 502. In this and other examples, the images couldundergo and pre-processing stage including the application of variousalgorithms to remove certain aspects of the image in order to be morereliably read by the recognition engines. In at least one embodiment, ifan image frame is out of focus, an image sharpening algorithm, such asan unsharp masking algorithm, can be applied to improve the imagequality before the image is sent to the recognition engine. In at leastone embodiment, a user could also be notified or alerted when the imagesthe user is capturing are of low quality, as may be due to movement, forexample, and the computing device could guide the user to keep thecamera still or otherwise instruct the user while capturing an image toimprove the image quality. In this example, the image is analyzed tolocate a region of text therein 504. In this example, the region of textis binarized 506. The text of the binarized region of text is thenconcurrently recognized with a first recognition engine and at least asecond recognition engine 508. Further, the recognition engines mayreturn associated bounding boxes for each string of characters withinthe recognized text. In this example, a confidence score for therecognized text is determined from the first recognition engine and theat least a second recognition engine 510. Based at least in part on acombination function of the confidence scores, a consensus string oftext is generated 512. In this example, the consensus string is providedeither for display or for use by an application, for a user of thecomputing device. Various other approaches for determining confidencecan be used as well as discussed or suggested elsewhere herein.

In an augmented reality (AR) application, a computing device can betrained to be interested in actionable text entity types, such as phonenumbers, URLs, and email addresses. For example, if a string appears tobe a phone number (based on the number and percentage of the digits inthe string certain characters will be replaced based collected confusionpatterns, such Z→2, O→0, and so on. The recognized text can then beprovided to an application executing on the computing devicecorresponding to the text entity type for use by the application. In theexample above, based on the pattern, the computing device can determinethat the test entity type is a phone number, recognize the text usingthe recognition process described above, and provide the number to aphone application for calling the number. Other text entity types canalso be used as described or suggested elsewhere herein.

Since a camera can capture multiple frames of a target in a continuousmanner, multiple image frames can be combined to increase accuracy ofthe recognized text from the recognition engines. For example, multipleoutputs from each recognition engine corresponding to multiple imagescan be compared to either verify image details or to capture detailsthat have been obscured or are missing in one image or frame. A word'sconfidence score, for example, can be a function, such as a summation,of individual image frame scores from multiple images. Once anaccumulated score of a word passes a certain threshold or a certain timelapses without any text exceeding a desired score threshold, forexample, the text can be presented to the user or relevant application.

Further, multiple image frames can be sent to the recognition engines atthe same time or a single image can be sent and, if the confidencescores from the recognized text for a respective image is below adetermined threshold, a subsequent image can be sent and processed. Inthe later example, a controller can continue to process additionalimages until a cumulative confidence score for the images reaches alevel above the determined threshold. For example, if the determinedthreshold is 0.80 and the confidence of a first image frame is 0.55, thecontroller have a second image frame processed. In this example, if theconfidence score for a combination function or summation of the firstand second image frames still does not at least equal 0.80, a thirdimage frame can be processed. Therefore, in one example, a computingdevice can send a server a single image to be processed by therecognition engines and, upon returning a confidence score below thethreshold, the computing can send a second image for processing.Accordingly, subsequent image frames can be processed until thecumulative confidence score at least equals the predetermined threshold.In this example, the first, second, and third image frames could beadjacent frames or they could be spaced apart by any number of frames,such as having 10, 50, or 100 frames between depending on factors suchas frame rate.

In a continuous image capture and processing mode, since information fora target string of text can be verified across multiple images,preprocessing techniques may not be necessary to efficiently andeffectively recognize the text. In a non-continuous mode, however, wherea single image is provided to the recognition engines, for example,preprocessing will be more important since there are not multiple framesto cross-reference therewith and as much accurate information must beextracted from the single image as possible. In either of these cases, acontroller can determine the appropriate amount of preprocessing for agiven situation.

Further, detecting text in an image can include more or fewer steps asdescribed above. For example, text detection in the image can includeperforming glyph detection on the image. The image can be separated intoregions of similar grayscale values that fall within predefined sizeconstraints called glyphs. Character classification can then beperformed, where any glyphs that are not characters are removed usingmachine learning algorithms or other similar algorithms. Pairfinding/word finding can then be performed, where the glyphs are groupedinto words and lines, and baseline estimation can then be performed onthe words and lines to estimate lines for the top and bottom points onthe words. Word splitting can then be performed, where the spacesbetween the glyphs can be examined to decide word boundaries used forevaluation or display purposes. Binarization can then be performed,where the regions are binarized to produce a crisp mask which caninclude any punctuation that may have been filtered out earlier due tothe minimum size constraint, for example.

Glyph detection can further include extracting maximally stable extremal(MSERs) regions from the image. An extremal region can be a set ofconnected pixels which have grayscale values above some threshold, andwhere the size of the region does not change significantly when thethreshold is varied over some range. In addition to being stable, theregions can contain most of the edge intensity found by computing agradient image beforehand. Regions that either have too many or too fewpixels, and any MSER whose aspect ratio is too different from normaltext or which has more than three child regions, should be ignored.

Character classification can further include extracting features fromeach MSER, the features including: Bounding Box Aspect Ratio (width overheight); Compactness (4 pi times area over perimeter squared); RawCompactness (4 pi times number of pixels over perimeter squared); StrokeWidth (estimated using distance transform) divided by width; StrokeWidth (estimated using distance transform) divided by height; Solidity(area over bounding box area); Convexity (convex hull perimeter overperimeter); Number of Holes (e.g., a ‘b’ has 1 hole, a ‘B’ has 2 holes,a ‘T’ has 0 holes). A fixed set of features can be selected and used totrain a classifier using a machine learning algorithm such as a supportvector machines (SVM) or AdaBoost. A classifier can be used to rejectmost non-characters from the list of characters, and an operating pointon the receiver operating characteristic (ROC) curve can be chosen sothat most characters are detected (ie. a low false negative rate), butwith a high false positive rate.

Further, pair finding can include sorting the remaining glyphs (MSERswhich appear to be characters) left to right, and all pairs which pass atest can be considered a possible character pair. The test compares thedistance between glyphs, vertical overlap of two glyphs, their relativeheight, width, stroke width, and intensity.

Accordingly, word line finding can further include treating each glyphas a vertex in a graph and each pair as an edge, then using an iterativedynamic programming algorithm to extract the best (e.g., the longest)sequence of edges, where the longest edges become word candidates.Additionally or alternatively, word line finding can include selectingglyphs from left to right after three glyphs are found to be in a goodsequence.

Base line estimation can further include estimating the slope of thebaseline using a clustering algorithm, then computing intercepts thatminimize the minimum distance between baselines and glyphs. Each wordcandidate can have at least two lines in the top and bottom points ofthe glyphs, and if two or more words appear to have the same baselines,they can be merged and the lines can be re-estimated. Further, inaccordance with an embodiment, glyph refinement can be performed afterbaseline estimation is performed, where all glyphs that are classifiedas non-text, but fit into the baseline configuration, are included.

In accordance with an embodiment, word splitting can further includeestimating the spaces between glyphs in each baseline and choosing athreshold, where any gap between characters greater than that thresholdcan be considered to be a word boundary (space) and can be marked assuch.

In accordance with an embodiment, binarization can further includebinarizing each region in the bounding box based at least in part on thethreshold used to compute the regions character and the regionscharacter's neighbors.

FIGS. 6A and 6B illustrate front and back views, respectively, of anexample electronic computing device 600 that can be used capture imagesand at least facilitate the recognition of text therein, in accordancewith various embodiments. Although a portable computing device (e.g., asmartphone, an electronic book reader, or tablet computer) is shown, itshould be understood that any device capable of receiving and processinginput can be used in accordance with various embodiments discussedherein. The devices can include, for example, desktop computers,notebook computers, electronic book readers, personal data assistants,cellular phones, video gaming consoles or controllers, television settop boxes, and portable media players, among others.

In this example, the computing device 600 has a display screen 602(e.g., an LCD element) operable to display information or image contentto one or more users or viewers of the device. The display screen ofsome embodiments displays information to the viewers facing the displayscreen (e.g., on the same side of the computing device as the displayscreen). The computing device in this example can include one or moreimaging elements, in this example including two image capture elements604 on the front of the device and at least one image capture element610 on the back of the device. It should be understood, however, thatimage capture elements could also, or alternatively, be placed on thesides or corners of the device, and that there can be any appropriatenumber of capture elements of similar or different types. Each imagecapture element 604 and 610 may be, for example, a camera, acharge-coupled device (CCD), a motion detection sensor or an infraredsensor, or other image capturing technology.

As discussed, the device can use the images (e.g., still or video)captured from the imaging elements 604 and 610 to generate athree-dimensional simulation of the surrounding environment (e.g., avirtual reality of the surrounding environment for display on thedisplay element of the device). Further, the device can utilize outputsfrom at least one of the image capture elements 604 and 610 to assist indetermining the location and/or orientation of a user and in recognizingnearby persons, objects, or locations. For example, if the user isholding the device, the captured image information can be analyzed(e.g., using mapping information about a particular area) to determinethe approximate location and/or orientation of the user. The capturedimage information may also be analyzed to recognize nearby persons,objects, or locations (e.g., by matching parameters or elements from themapping information).

The computing device can also include at least one microphone or otheraudio capture elements capable of capturing audio data, such as wordsspoken by a user of the device, music being hummed by a person near thedevice, or audio being generated by a nearby speaker or other suchcomponent, although audio elements are not required in at least somedevices. In this example there are three microphones, one microphone 608on the front side, one microphone 612 on the back, and one microphone606 on or near a top or side of the device. In some devices there may beonly one microphone, while in other devices there might be at least onemicrophone on each side and/or corner of the device, or in otherappropriate locations.

The device 600 in this example also includes one or more orientation- orposition-determining elements 618 operable to provide information suchas a position, direction, motion, or orientation of the device. Theseelements can include, for example, accelerometers, inertial sensors,electronic gyroscopes, and electronic compasses.

The example device also includes at least one communication mechanism614, such as may include at least one wired or wireless componentoperable to communicate with one or more electronic devices. The devicealso includes a power system 616, such as may include a battery operableto be recharged through conventional plug-in approaches, or throughother approaches such as capacitive charging through proximity with apower mat or other such device. Various other elements and/orcombinations are possible as well within the scope of variousembodiments.

FIG. 7 illustrates a set of basic components of an electronic computingdevice 700 such as the device 600 described with respect to FIG. 6. Inthis example, the device includes at least one processing unit 702 forexecuting instructions that can be stored in a memory device or element704. As would be apparent to one of ordinary skill in the art, thedevice can include many types of memory, data storage, orcomputer-readable media, such as a first data storage for programinstructions for execution by the processing unit(s) 702, the same orseparate storage can be used for images or data, a removable memory canbe available for sharing information with other devices, and any numberof communication approaches can be available for sharing with otherdevices.

The device typically will include some type of display element 706, suchas a touch screen, electronic ink (e-ink), organic light emitting diode(OLED) or liquid crystal display (LCD), although devices such asportable media players might convey information via other means, such asthrough audio speakers.

As discussed, the device in many embodiments will include at least oneimaging element 708, such as one or more cameras that are able tocapture images of the surrounding environment and that are able to imagea user, people, text, or objects in the vicinity of the device. Theimage capture element can include any appropriate technology, such as aCCD image capture element having a sufficient resolution, focal range,and viewable area to capture an image of the user when the user isoperating the device. Methods for capturing images using a cameraelement with a computing device are well known in the art and will notbe discussed herein in detail. It should be understood that imagecapture can be performed using a single image, multiple images, periodicimaging, continuous image capturing, image streaming, etc. Further, adevice can include the ability to start and/or stop image capture, suchas when receiving a command from a user, application, or other device.

The example computing device 700 also includes at least one orientationdetermining element 710 able to determine and/or detect orientationand/or movement of the device. Such an element can include, for example,an accelerometer or gyroscope operable to detect movement (e.g.,rotational movement, angular displacement, tilt, position, orientation,motion along a non-linear path, etc.) of the device 700. An orientationdetermining element can also include an electronic or digital compass,which can indicate a direction (e.g., north or south) in which thedevice is determined to be pointing (e.g., with respect to a primaryaxis or other such aspect).

As discussed, the device in many embodiments will include at least apositioning element 712 for determining a location of the device (or theuser of the device). A positioning element can include or comprise a GPSor similar location-determining elements operable to determine relativecoordinates for a position of the device. As mentioned above,positioning elements may include wireless access points, base stations,etc. that may either broadcast location information or enabletriangulation of signals to determine the location of the device. Otherpositioning elements may include QR codes, barcodes, RFID tags, NFCtags, etc. that enable the device to detect and receive locationinformation or identifiers that enable the device to obtain the locationinformation (e.g., by mapping the identifiers to a correspondinglocation). Various embodiments can include one or more such elements inany appropriate combination.

As mentioned above, some embodiments use the element(s) to track thelocation of a device. Upon determining an initial position of a device(e.g., using GPS), the device of some embodiments may keep track of thelocation of the device by using the element(s), or in some instances, byusing the orientation determining element(s) as mentioned above, or acombination thereof. As should be understood, the algorithms ormechanisms used for determining a position and/or orientation can dependat least in part upon the selection of elements available to the device.

The example device also includes one or more wireless components 714operable to communicate with one or more electronic devices within acommunication range of the particular wireless channel. The wirelesschannel can be any appropriate channel used to enable devices tocommunicate wirelessly, such as Bluetooth, cellular, NFC, or Wi-Fichannels. It should be understood that the device can have one or moreconventional wired communications connections as known in the art.

The device also includes a power system 716, such as may include abattery operable to be recharged through conventional plug-inapproaches, or through other approaches such as capacitive chargingthrough proximity with a power mat or other such device. Various otherelements and/or combinations are possible as well within the scope ofvarious embodiments.

In some embodiments the device can include at least one additional inputdevice 718 able to receive conventional input from a user. Thisconventional input can include, for example, a push button, touch pad,touch screen, wheel, joystick, keyboard, mouse, keypad, or any othersuch device or element whereby a user can input a command to the device.These I/O devices could even be connected by a wireless infrared orBluetooth or other link as well in some embodiments. Some devices alsocan include a microphone or other audio capture element that acceptsvoice or other audio commands. For example, a device might not includeany buttons at all, but might be controlled only through a combinationof visual and audio commands, such that a user can control the devicewithout having to be in contact with the device.

In some embodiments, a device can include the ability to activate and/ordeactivate detection and/or command modes, such as when receiving acommand from a user or an application, or retrying to determine an audioinput or video input, etc. In some embodiments, a device can include aninfrared detector or motion sensor, for example, which can be used toactivate one or more detection modes. For example, a device might notattempt to detect or communicate with devices when there is not a userin the room. If an infrared detector (i.e., a detector with one-pixelresolution that detects changes in state) detects a user entering theroom, for example, the device can activate a detection or control modesuch that the device can be ready when needed by the user, but conservepower and resources when a user is not nearby.

A computing device, in accordance with various embodiments, may includea light-detecting element that is able to determine whether the deviceis exposed to ambient light or is in relative or complete darkness. Suchan element can be beneficial in a number of ways. In certainconventional devices, a light-detecting element is used to determinewhen a user is holding a cell phone up to the user's face (causing thelight-detecting element to be substantially shielded from the ambientlight), which can trigger an action such as the display element of thephone to temporarily shut off (since the user cannot see the displayelement while holding the device to the user's ear). The light-detectingelement could be used in conjunction with information from otherelements to adjust the functionality of the device. For example, if thedevice is unable to detect a user's view location and a user is notholding the device but the device is exposed to ambient light, thedevice might determine that it has likely been set down by the user andmight turn off the display element and disable certain functionality. Ifthe device is unable to detect a user's view location, a user is notholding the device and the device is further not exposed to ambientlight, the device might determine that the device has been placed in abag or other compartment that is likely inaccessible to the user andthus might turn off or disable additional features that might otherwisehave been available. In some embodiments, a user must either be lookingat the device, holding the device or have the device out in the light inorder to activate certain functionality of the device. In otherembodiments, the device may include a display element that can operatein different modes, such as reflective (for bright situations) andemissive (for dark situations). Based on the detected light, the devicemay change modes.

Using the microphone, the device can disable other features for reasonssubstantially unrelated to power savings. For example, the device canuse voice recognition to determine people near the device, such aschildren, and can disable or enable features, such as Internet access orparental controls, based thereon. Further, the device can analyzerecorded noise to attempt to determine an environment, such as whetherthe device is in a car or on a plane, and that determination can help todecide which features to enable/disable or which actions are taken basedupon other inputs. If voice recognition is used, words can be used asinput, either directly spoken to the device or indirectly as picked upthrough conversation. For example, if the device determines that it isin a car, facing the user and detects a word such as “hungry” or “eat,”then the device might turn on the display element and displayinformation for nearby restaurants, etc. A user can have the option ofturning off voice recording and conversation monitoring for privacy andother such purposes.

In some of the above examples, the actions taken by the device relate todeactivating certain functionality for purposes of reducing powerconsumption. It should be understood, however, that actions cancorrespond to other functions that can adjust similar and otherpotential issues with use of the device. For example, certain functions,such as requesting Web page content, searching for content on a harddrive and opening various applications, can take a certain amount oftime to complete. For devices with limited resources, or that have heavyusage, a number of such operations occurring at the same time can causethe device to slow down or even lock up, which can lead toinefficiencies, degrade the user experience and potentially use morepower.

In order to address at least some of these and other such issues,approaches in accordance with various embodiments can also utilizeinformation such as user gaze direction to activate resources that arelikely to be used in order to spread out the need for processingcapacity, memory space and other such resources.

In some embodiments, the device can have sufficient processingcapability, and the imaging element and associated analyticalalgorithm(s) may be sensitive enough to distinguish between the motionof the device, motion of a user's head, motion of the user's eyes andother such motions, based on the captured images alone. In otherembodiments, such as where it may be desirable for the process toutilize a fairly simple imaging element and analysis approach, it can bedesirable to include at least one orientation determining element thatis able to determine a current orientation of the device. In oneexample, the at least one orientation determining element is at leastone single- or multi-axis accelerometer that is able to detect factorssuch as three-dimensional position of the device and the magnitude anddirection of movement of the device, as well as vibration, shock, etc.Methods for using elements such as accelerometers to determineorientation or movement of a device are also known in the art and willnot be discussed herein in detail. Other elements for detectingorientation and/or movement can be used as well within the scope ofvarious embodiments for use as the orientation determining element. Whenthe input from an accelerometer or similar element is used along withthe input from the camera, the relative movement can be more accuratelyinterpreted, allowing for a more precise input and/or a less compleximage analysis algorithm.

When using an imaging element of the computing device to detect motionof the device and/or user, for example, the computing device can use thebackground in the images to determine movement. For example, if a userholds the device at a fixed orientation (e.g. distance, angle, etc.) tothe user and the user changes orientation to the surroundingenvironment, analyzing an image of the user alone will not result indetecting a change in an orientation of the device. Rather, in someembodiments, the computing device can still detect movement of thedevice by recognizing the changes in the background imagery behind theuser. So, for example, if an object (e.g. a window, picture, tree, bush,building, car, etc.) moves to the left or right in the image, the devicecan determine that the device has changed orientation, even though theorientation of the device with respect to the user has not changed. Inother embodiments, the device may detect that the user has moved withrespect to the device and adjust accordingly. For example, if the usertilts their head to the left or right with respect to the device, thecontent rendered on the display element may likewise tilt to keep thecontent in orientation with the user.

As discussed, different approaches can be implemented in variousenvironments in accordance with the described embodiments. For example,FIG. 8 illustrates an example of an environment 800 for implementingaspects in accordance with various embodiments. As will be appreciated,although a Web-based environment is used for purposes of explanation,different environments may be used, as appropriate, to implement variousembodiments. The system includes an electronic client device 802, whichcan include any appropriate device operable to send and receiverequests, messages or information over an appropriate network 804 andconvey information back to a user of the device. Examples of such clientdevices include personal computers, cell phones, handheld messagingdevices, laptop computers, set-top boxes, personal data assistants,electronic book readers and the like. The network can include anyappropriate network, including an intranet, the Internet, a cellularnetwork, a local area network or any other such network or combinationthereof. The network could be a “push” network, a “pull” network, or acombination thereof. In a “push” network, one or more of the serverspush out data to the client device. In a “pull” network, one or more ofthe servers send data to the client device upon request for the data bythe client device. Components used for such a system can depend at leastin part upon the type of network and/or environment selected. Protocolsand components for communicating via such a network are well known andwill not be discussed herein in detail. Communication over the networkcan be enabled via wired or wireless connections and combinationsthereof. In this example, the network includes the Internet, as theenvironment includes a Web server 806 for receiving requests and servingcontent in response thereto, although for other networks, an alternativedevice serving a similar purpose could be used, as would be apparent toone of ordinary skill in the art.

The illustrative environment includes at least one application server808 and a data store 810. It should be understood that there can beseveral application servers, layers or other elements, processes orcomponents, which may be chained or otherwise configured, which caninteract to perform tasks such as obtaining data from an appropriatedata store. As used herein, the term “data store” refers to any deviceor combination of devices capable of storing, accessing and retrievingdata, which may include any combination and number of data servers,databases, data storage devices and data storage media, in any standard,distributed or clustered environment. The application server 808 caninclude any appropriate hardware and software for integrating with thedata store 810 as needed to execute aspects of one or more applicationsfor the client device and handling a majority of the data access andbusiness logic for an application. The application server providesaccess control services in cooperation with the data store and is ableto generate content such as text, graphics, audio and/or video to betransferred to the user, which may be served to the user by the Webserver 806 in the form of HTML, XML or another appropriate structuredlanguage in this example. The handling of all requests and responses, aswell as the delivery of content between the client device 802 and theapplication server 808, can be handled by the Web server 806. It shouldbe understood that the Web and application servers are not required andare merely example components, as structured code discussed herein canbe executed on any appropriate device or host machine as discussedelsewhere herein.

The data store 810 can include several separate data tables, databasesor other data storage mechanisms and media for storing data relating toa particular aspect. For example, the data store illustrated includesmechanisms for storing content (e.g., production data) 812 and userinformation 816, which can be used to serve content for the productionside. The data store is also shown to include a mechanism for storinglog or session data 814. It should be understood that there can be manyother aspects that may need to be stored in the data store, such as pageimage information and access rights information, which can be stored inany of the above listed mechanisms as appropriate or in additionalmechanisms in the data store 810. The data store 810 is operable,through logic associated therewith, to receive instructions from theapplication server 808 and obtain, update or otherwise process data inresponse thereto. In one example, a user might submit a search requestfor a certain type of item. In this case, the data store might accessthe user information to verify the identity of the user and can accessthe catalog detail information to obtain information about items of thattype. The information can then be returned to the user, such as in aresults listing on a Web page that the user is able to view via abrowser on the user device 802. Information for a particular item ofinterest can be viewed in a dedicated page or window of the browser.

Each server typically will include an operating system that providesexecutable program instructions for the general administration andoperation of that server and typically will include computer-readablemedium storing instructions that, when executed by a processor of theserver, allow the server to perform its intended functions. Suitableimplementations for the operating system and general functionality ofthe servers are known or commercially available and are readilyimplemented by persons having ordinary skill in the art, particularly inlight of the disclosure herein.

The environment in one embodiment is a distributed computing environmentutilizing several computer systems and components that areinterconnected via communication links, using one or more computernetworks or direct connections. However, it will be appreciated by thoseof ordinary skill in the art that such a system could operate equallywell in a system having fewer or a greater number of components than areillustrated in FIG. 8. Thus, the depiction of the system 800 in FIG. 8should be taken as being illustrative in nature and not limiting to thescope of the disclosure.

The various embodiments can be further implemented in a wide variety ofoperating environments, which in some cases can include one or more usercomputers or computing devices which can be used to operate any of anumber of applications. User or client devices can include any of anumber of general purpose personal computers, such as desktop or laptopcomputers running a standard operating system, as well as cellular,wireless and handheld devices running mobile software and capable ofsupporting a number of networking and messaging protocols. Such a systemcan also include a number of workstations running any of a variety ofcommercially-available operating systems and other known applicationsfor purposes such as development and database management. These devicescan also include other electronic devices, such as dummy terminals,thin-clients, gaming systems and other devices capable of communicatingvia a network.

Most embodiments utilize at least one network that would be familiar tothose skilled in the art for supporting communications using any of avariety of commercially-available protocols, such as TCP/IP, OSI, FTP,UPnP, NFS, CIFS and AppleTalk. The network can be, for example, a localarea network, a wide-area network, a virtual private network, theInternet, an intranet, an extranet, a public switched telephone network,an infrared network, a wireless network and any combination thereof.

In embodiments utilizing a Web server, the Web server can run any of avariety of server or mid-tier applications, including HTTP servers, FTPservers, CGI servers, data servers, Java servers and businessapplication servers. The server(s) may also be capable of executingprograms or scripts in response requests from user devices, such as byexecuting one or more Web applications that may be implemented as one ormore scripts or programs written in any programming language, such asJava®, C, C# or C++ or any scripting language, such as Perl, Python orTCL, as well as combinations thereof. The server(s) may also includedatabase servers, including without limitation those commerciallyavailable from Oracle®, Microsoft®, Sybase® and IBM®.

The environment can include a variety of data stores and other memoryand storage media as discussed above. These can reside in a variety oflocations, such as on a storage medium local to (and/or resident in) oneor more of the computers or remote from any or all of the computersacross the network. In a particular set of embodiments, the informationmay reside in a storage-area network (SAN) familiar to those skilled inthe art. Similarly, any necessary files for performing the functionsattributed to the computers, servers or other network devices may bestored locally and/or remotely, as appropriate. Where a system includescomputerized devices, each such device can include hardware elementsthat may be electrically coupled via a bus, the elements including, forexample, at least one central processing unit (CPU), at least one inputdevice (e.g., a mouse, keyboard, controller, touch-sensitive displayelement or keypad) and at least one output device (e.g., a displaydevice, printer or speaker). Such a system may also include one or morestorage devices, such as disk drives, optical storage devices andsolid-state storage devices such as random access memory (RAM) orread-only memory (ROM), as well as removable media devices, memorycards, flash cards, etc.

Such devices can also include a computer-readable storage media reader,a communications device (e.g., a modem, a network card (wireless orwired), an infrared communication device) and working memory asdescribed above. The computer-readable storage media reader can beconnected with, or configured to receive, a computer-readable storagemedium representing remote, local, fixed and/or removable storagedevices as well as storage media for temporarily and/or more permanentlycontaining, storing, transmitting and retrieving computer-readableinformation. The system and various devices also typically will includea number of software applications, modules, services or other elementslocated within at least one working memory device, including anoperating system and application programs such as a client applicationor Web browser. It should be appreciated that alternate embodiments mayhave numerous variations from that described above. For example,customized hardware might also be used and/or particular elements mightbe implemented in hardware, software (including portable software, suchas applets) or both. Further, connection to other computing devices suchas network input/output devices may be employed.

Storage media and computer readable media for containing code, orportions of code, can include any appropriate media known or used in theart, including storage media and communication media, such as but notlimited to volatile and non-volatile, removable and non-removable mediaimplemented in any method or technology for storage and/or transmissionof information such as computer readable instructions, data structures,program modules or other data, including RAM, ROM, EEPROM, flash memoryor other memory technology, CD-ROM, digital versatile disk (DVD) orother optical storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices or any other medium which canbe used to store the desired information and which can be accessed by asystem device. Based on the disclosure and teachings provided herein, aperson of ordinary skill in the art will appreciate other ways and/ormethods to implement the various embodiments.

The specification and drawings are, accordingly, to be regarded in anillustrative rather than a restrictive sense. It will, however, beevident that various modifications and changes may be made thereuntowithout departing from the broader spirit and scope of the invention asset forth in the claims.

What is claimed is:
 1. A computer-implemented method, comprising:obtaining an image captured by a camera of a computing device; analyzingthe image to locate a region of text represented in the image;binarizing the region of text to generate a binarized region;recognizing text of the binarized region with a first optical characterrecognition (OCR) engine and a second (OCR) engine to identify firstrecognized text and second recognized text; tuning a first processingspeed associated with the first (OCR) engine and a second processingspeed associated with the second OCR engine such that the firstprocessing speed and the second processing speed are equal to within apredefined deviation; determining a first confidence score for the firstrecognized text and a second confidence score for the second recognizedtext by: searching a database for matching words within the firstrecognized text and the second recognized text; increasing the firstconfidence score for the first recognized text based on matching astring of characters in the first recognized text or the secondrecognized text to at least one first word in the database; andincreasing the second confidence for the second recognized text based onmatching a second string of characters in the second recognized text toat least one of the first word or a second word in the database; andapplying a combination function of the first confidence score and thesecond confidence score to generate a consensus string of text, theconsensus string of text comprising at least a portion of at least oneof the first recognized text or the second recognized text.
 2. Thecomputer-implemented method of claim 1, wherein the first processingspeed and the second processing speed are tuned to reduce a processinglatency associated with a combination of the first OCR engine and thesecond OCR engine.
 3. The computer-implemented method of claim 1,further comprising: determining that the combination function of thefirst confidence score and the second confidence score is below athreshold; and processing the binarized region with a third OCR engine.4. The computer-implemented method of claim 1, further comprising:applying a first bounding box to the binarized region; applying a secondbounding box to the binarized region; and aligning the first boundingbox of the first recognized text from the first OCR engine with thesecond bounding box of the second recognized text from the at least asecond OCR engine.
 5. A computer-implemented method, comprising:analyzing an image to locate a region of text in the image; selecting asubset of the image associated with the region of text represented inthe image; the subset of the image to generate a binarized region;tuning a first processing speed associated with a first opticalcharacter recognition (OCR) engine and a second processing speedassociated with a second OCR engine such that the first processing speedand the second processing speed are equal to within a predefineddeviation; recognizing the text represented in the image with the firstOCR engine to yield a first recognized text; recognizing the textrepresented in the image with the second OCR engine to yield a secondrecognized text, the first OCR engine being different relative to thesecond OCR engine; determining a first confidence score for the firstrecognized text and a second confidence score for the second recognizedtext by: searching a database for matching words within the firstrecognized text and the second recognized text; increasing the firstconfidence score for the first recognized text based on matching astring of characters in the first recognized text or the secondrecognized text to at least one first word in the database; andincreasing the second confidence for the second recognized text based onmatching a second string of characters in the second recognized text toat least one of the first word or a second word in the database; andapplying a combination function of the first confidence score and thesecond confidence score to generate a consensus string of textcomprising at least a portion of at least one of the first recognizedtext from the first OCR engine or the second recognized text from thesecond OCR engine.
 6. The computer-implemented method of claim 5,further comprising: recognizing second text represented in a secondimage with the first OCR engine to yield a third recognized text;recognizing the second text in the second image with the second OCRengine to yield fourth recognized text; determining a third confidencescore for the third recognized text from the first OCR engine and afourth confidence score for the fourth recognized text from the secondOCR engine; and applying a combination function of the first confidencescore, the second confidence score, the third confidence score, and thefourth confidence score to generate the consensus string of textcomprising at least a portion of at least one of the first recognizedtext, the second recognized text, the third recognized text, or thefourth recognized text.
 7. The computer-implemented method of claim 5,wherein the first processing speed and the second processing speed aretuned to reduce a processing latency associated with a combination ofthe first OCR engine and the second OCR engine.
 8. Thecomputer-implemented method of claim 5, further comprising: determiningthat the combination function of the first confidence score and thesecond confidence score is below a threshold; and processing thebinarized region with a third OCR engine.
 9. The computer-implementedmethod of claim 5, further comprising: applying a first bounding box tothe binarized region; applying a second bounding box to the binarizedregion; and aligning the first bounding box of the first recognized textfrom the first OCR engine with the second bounding box of the secondrecognized text from the second OCR engine.
 10. The computer-implementedmethod of claim 5, wherein the image is captured by at least one cameraof a portable computing device.
 11. The computer-implemented method ofclaim 5, further comprising: communicating the binarized region to thefirst OCR engine and the second OCR engine.
 12. A computing device,comprising: a processor; a display screen; and memory includinginstructions that, when executed by the processor, cause the computingdevice to: analyze an image to locate a region of text in the image;select a subset of the image associated with the region of textrepresented in the image; binarize the subset of the image to generate abinarized region; tune a first processing speed associated with a firstOCR engine and a second processing speed associated with a second OCRengine such that the first processing speed and the second processingspeed are equal to within a predefined deviation; recognize the textrepresented in the image with the first OCR engine to yield a firstrecognized text; recognize the text represented in the image with thesecond OCR engine to yield a second recognized text, the first OCRengine being different relative to the second OCR engine; determine afirst confidence score for the first recognized text and a secondconfidence score for the second recognized text by: searching a databasefor matching words within the first recognized text and the secondrecognized text; increasing the first confidence score for the firstrecognized text based on matching a string of characters in the firstrecognized text or the second recognized text to at least one first wordin the database; and increasing the second confidence for the secondrecognized text based on matching a second string of characters in thesecond recognized text to at least one of the first word or a secondword in the database; and apply a combination function of the firstconfidence score and the second confidence score to generate a consensusstring of text comprising at least a portion of at least one of thefirst recognized text from the first OCR engine or the second recognizedtext from the second OCR engine.
 13. The computing device of claim 12,wherein the memory includes instructions that, when executed by theprocessor, further cause the computing device to: recognize second textrepresented in a second image with the first OCR engine to yield a thirdrecognized text; recognize the second text represented in the secondimage with the second OCR engine to yield fourth recognized text;determine a third confidence score for the third recognized text fromthe first OCR engine and a fourth confidence score for the fourthrecognized text from the second OCR engine; and apply a combinationfunction of the first confidence score, the second confidence score, thethird confidence score, and the fourth confidence score to generate theconsensus string of text comprising at least a portion of at least oneof the first recognized text, the second recognized text, the thirdrecognized text, or the fourth recognized text.
 14. The computing deviceof claim 12, wherein the first processing speed and the secondprocessing speed are tuned to reduce a processing latency associatedwith a combination of the first OCR engine and the second OCR engine.15. The computing device of claim 12, wherein the memory includesinstructions that, when executed by the processor, further cause thecomputing device to: determining that the combination function of thefirst confidence score and the second confidence score is below athreshold; and processing the binarized region with a third OCR engine.16. The computing device of claim 12, wherein the memory includesinstructions that, when executed by the processor, further cause thecomputing device to: apply a first bounding box to the binarized region;apply a second bounding box to the binarized region; and align the firstbounding box of the first recognized text from the first OCR engine withthe second bounding box of the second recognized text from the secondOCR engine.
 17. The computing device of claim 12, further comprising atleast one camera that captures the image in a capture mode, the capturemode including at least one of a single image capture mode, a multipleimage capture mode, a periodic imaging capture mode, a continuous imagecapturing mode, and an image streaming capture mode.